I am aware of the fact that when contrasting two conditions, the epochs
within each condition should be equalized before reconstructing in source
space, otherwise the SNR would be different between the two conditions.
That also means that if two subjects have a different number of epochs, the
SNR of each subject will be different. It is often the case that the number
of epochs doubled between subjects (50 vs. 100) so I guess that it is an
issue when looking at group averaged stc for instance.
A possible solution would be to normalize the stc to correct for the number
of trials. What do you think of that? What would be the proper
normalization?
I I'm understanding you correctly this should not be an issue for evoked
data as long as you are using the .average method that will tell the
inverse routines how to scale the data via its .nave attribute.
Did you have any particular problems?
So does that mean that we need not to equalize epochs between conditions
(within one subject)?
2016-03-03 16:23 GMT+01:00 Denis-Alexander Engemann <
denis.engemann at gmail.com>:
Hi Laetitia,
I I'm understanding you correctly this should not be an issue for evoked
data as long as you are using the .average method that will tell the
inverse routines how to scale the data via its .nave attribute.
Did you have any particular problems?
Cheers,
Denis
Dear MNE-users,
I am aware of the fact that when contrasting two conditions, the epochs
within each condition should be equalized before reconstructing in source
space, otherwise the SNR would be different between the two conditions.
That also means that if two subjects have a different number of epochs, the
SNR of each subject will be different. It is often the case that the number
of epochs doubled between subjects (50 vs. 100) so I guess that it is an
issue when looking at group averaged stc for instance.
A possible solution would be to normalize the stc to correct for the
number of trials. What do you think of that? What would be the proper
normalization?
Thanks,
Best,
Laetitia G.
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Yes that's the idea. Amplitudes of the inverse solution are scaled by the
number of trials (.nave). This should work as an heuristic unless you
compare rare to frequent events (e.g. oddball).
Hope this helps.
Ok, thanks, I was still applying the old advice of equalizing epochs
between conditions! But so, why in certain statistical examples of the
website, you're still equalizing conditions (like in the Reapeated-measures
ANOVA in sources)?
2016-03-03 16:43 GMT+01:00 Denis-Alexander Engemann <
denis.engemann at gmail.com>:
Yes that's the idea. Amplitudes of the inverse solution are scaled by the
number of trials (.nave). This should work as an heuristic unless you
compare rare to frequent events (e.g. oddball).
Hope this helps.
So does that mean that we need not to equalize epochs between conditions
(within one subject)?
2016-03-03 16:23 GMT+01:00 Denis-Alexander Engemann <
denis.engemann at gmail.com>:
Hi Laetitia,
I I'm understanding you correctly this should not be an issue for evoked
data as long as you are using the .average method that will tell the
inverse routines how to scale the data via its .nave attribute.
Did you have any particular problems?
Cheers,
Denis
Dear MNE-users,
I am aware of the fact that when contrasting two conditions, the epochs
within each condition should be equalized before reconstructing in source
space, otherwise the SNR would be different between the two conditions.
That also means that if two subjects have a different number of epochs, the
SNR of each subject will be different. It is often the case that the number
of epochs doubled between subjects (50 vs. 100) so I guess that it is an
issue when looking at group averaged stc for instance.
A possible solution would be to normalize the stc to correct for the
number of trials. What do you think of that? What would be the proper
normalization?
Thanks,
Best,
Laetitia G.
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it is
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e-mail
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but does not contain patient information, please contact the sender and
properly
dispose of the e-mail.
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If you plan to take the absolute value of the data (or the norm of the
three components) it's advisable to equalize the event counts. This
nonlinear operation takes what is typically thought of as being zero-mean,
normally distributed noise, and turns it into a folded normal distribution.
A folded normal distribution has a mean that is dependent on the standard
deviation of the un-folded version of the distribution. Different trial
counts yield different effective noise standard deviations, which then
become different noise *means* once the distribution gets folded by the
abs()-like operation.
In other words, if you don't equalize the trial counts before a nonlinear
norm operation, you can end up with potentially biased estimates. At one
point I tried correcting for this bias, but was unsuccessful -- the noise
distribution, even if turned zero-mean, was skewed. Perhaps there is some
good way to deal with it, but subsampling the data (equalizing trial
counts) has seemed to be the safest thus far. It's probably not a big deal
if it's 35 vs 40 trials, but if you have an unbalanced design of e.g. 30
trials in one condition and 60 trials in another, it's more likely to
matter.
2016-03-03 17:07 GMT+01:00 Denis-Alexander Engemann <
denis.engemann at gmail.com>:
I think there is actually no strong reason for that. Maybe some
unintentionally copied line.
Ok, thanks, I was still applying the old advice of equalizing epochs
between conditions! But so, why in certain statistical examples of the
website, you're still equalizing conditions (like in the Reapeated-measures
ANOVA in sources)?
2016-03-03 16:43 GMT+01:00 Denis-Alexander Engemann <
denis.engemann at gmail.com>:
Yes that's the idea. Amplitudes of the inverse solution are scaled by
the number of trials (.nave). This should work as an heuristic unless you
compare rare to frequent events (e.g. oddball).
Hope this helps.
So does that mean that we need not to equalize epochs between
conditions (within one subject)?
2016-03-03 16:23 GMT+01:00 Denis-Alexander Engemann <
denis.engemann at gmail.com>:
Hi Laetitia,
I I'm understanding you correctly this should not be an issue for
evoked data as long as you are using the .average method that will tell the
inverse routines how to scale the data via its .nave attribute.
Did you have any particular problems?
Cheers,
Denis
Dear MNE-users,
I am aware of the fact that when contrasting two conditions, the
epochs within each condition should be equalized before reconstructing in
source space, otherwise the SNR would be different between the two
conditions. That also means that if two subjects have a different number of
epochs, the SNR of each subject will be different. It is often the case
that the number of epochs doubled between subjects (50 vs. 100) so I guess
that it is an issue when looking at group averaged stc for instance.
A possible solution would be to normalize the stc to correct for the
number of trials. What do you think of that? What would be the proper
normalization?
Thanks,
Best,
Laetitia G.
_______________________________________________
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